The long-term goal of this project is to advance the state of the art in the automated measurement and recognition of leukocytes, by computer assisted, high resolution image analysis. Part of the project will concentrate on developing improved techniques for automated recognition of leukocytes, and it is expected that the increased accuracy and efficiency of these methods will allow the routine counting of larger cell samples, with a resulting higher sensitivity for abnormal screening. It is further expected that an automated system based on these techniques will be able to perform with greater accuracy and sensitivity than the average technician, and not merely duplicate the manual technique which is well-known to have great shortcomings. Another most important goal of the project is to develop techniques that allow the extraction of usable information from the cells, other than simply the number of each type present. Global parameters such as average cell size or color, may be important indicators of disease that are not routinely measured at present. In addition, we propose to extend our techniques to the following of patients with treated as well as untreated disorders, where such treatments or disorders produce modifications in the morphology of the leukocytes. Although peripheral blood samples will be of primary interest in routine differential counting applications, we propose to extend our methodology to bone marrow samples as well, both for possible clincial applications and for basic research applications. This will involve the development of new and more consistent preparation techniques for bone marrow samples. Specific tasks include the collection of a comprehensive digitized data base of over 8,000 cells, each cell digitized at (at least) four wavelengths; development of innovative scene segmentation techniques to isolate and partition cell images; development of new measurable morphologic parameters to describe cells; development of better classifiers to perform cell identification; and the correlation of measured parameters with disease states.